Uncertainty in demand, market dynamics, and supply chain disruptions often lead to forecasting errors, causing excess inventory or stockouts. The key challenge is to balance adequate inventory levels against the costs and risks of uncertainty. Optimal inventory management requires robust forecasting, agile supply chain processes, effective risk management, and the capacity for swift inventory adjustments.
Forecasts based solely on historical data are merely guesses if they fail to consider various uncertainties.
By modeling complex relationships between demand, supply and market dynamics while incorporating uncertainties present in demand forecasting, AI Simulation significantly improves supply plans to align with the anticipated demand fluctuations.
With advanced experiments such as sensitivity analysis, supply chain managers can analyze how changes in different variables affect inventory management key performance indicators.
Businesses can find the optimal balance between inventory costs and customer satisfaction by acting on various areas of the supply chain at a global scale.
![[eBook] Tackling supply chain challenges with AI Simulation](png/sc-e-book-3-350x350.png)
